In this episode of A4N, I have a special announcement! While the A4N podcast will be going on indefinite hiatus, it is because I am now hosting the SuperDataScience Podcast. If you enjoyed A4N then you're sure to enjoy the SuperDataScience podcast, which publishes twice every week on Tuesdays and Fridays!

You can check it out here: https://www.superdatascience.com/podcast

In addition, a raw video feed of the podcast is available on YouTube.

In this episode of A4N, Dr. Rasmus Rothe joins us to discuss Merantix, the world’s first AI Venture Studio, which he co-founded in 2016. We discuss how Merantix companies are shaping the future by applying machine learning to automating cancer detection, training self-driving cars, and more!

Dr. Rasmus Rothe is a German native, and co-founder of Merantix, the world’s first AI-focused venture studio. Merantix has already launched three successful AI-driven companies with three more operating in stealth, and raised an additional EUR 25 MM in 2020 to continue to apply world-class AI research to solving practical issues. Rasmus published 15+ papers on deep learning while attending Oxford, Princeton and ETH Zurich, where he received his Ph.D. in computer vision and deep learning. Before founding Merantix, Rasmus worked for BCG, Google, and built a deep learning service with 150m+ users. He is also a founding board member of the German AI Association.

Reference links:

1:53 Previous episode of A4N

2:05 racy doctoral research

2:28 Merantix

5:27 Deep Learning Illustrated

14:35 Merantix raised 25m euros

20:00 Vara Healthcare

20:10 Vara raised 6.5m euros ($7m) in Series A venture capital

31:41 Siasearch

38:50 Dr. Alex Flint's start-up, Zippy, was acquired by General Motors' self-driving car unit in 2018

41:30 German A.I. Association

46:20 Dr. Rasmus Rothe's LinkedIn

46:28 Rasmus on Twitter

47:15 Jon Krohn on LinkedIn

47:40 Jon Krohn's email newsletter signup on his homepage

48:00 Jon on Twitter

In this episode of our A4N podcast, our guest host Kirill Eremenko joins us to discuss SuperDataScience, his thriving data-science education business, and Vince introduces us to machine learning projects being applied to understand -- and preserve -- marine life in the oceans.

Our special guest today is Kirill Eremenko. Kirill is Russian-born Australian, and Founder and CEO of SuperDataScience , an online educational portal for Data Scientists. Their mission is to “Make The Complex Simple,” and become the biggest learning portal for Data Science enthusiasts. Ever. He is also the Co-Founder of BlueLifeAI, Founder of the DataScienceGo conference, and hosts his own podcast, the SuperDataScience Podcast!

Part I: Scaling a Global Data Business with Kirill Eremenko

4:25 OmniFocus

4:36 SuperDataScience

5:25 Udemy

8:00 The Productivity Project

8:05 Deep Work

8:20 The Great CEO Within

9:20 Peter Akkies: Get Stuff Done with OmniFocus 3

17:57 DataScienceGO Virtual

22:10 Fake Ad for “TPML”

Part 2: 23:00 Saving the Oceans with Machine Learning

24:30 A.I. Is Helping Scientists Understand an Ocean’s Worth of Data

30:18 Some like it hot - visual guidance for preference prediction

31:15 Merantix

32:10 Wirewax

33:05 Scale

36:00 VGGish

39:57 University of San Diego’s FRED

41:18 Jon Krohn’s LinkedIn

41:23 Kirill Eremenko’s LinkedIn

41:23 Vince Petaccio II’s LinkedIn

41:28 Twitter @JonKrohnLearns

41:49 jon@jonkrohn.com

41:52 email newsletter at jonkrohn.com

42:00 Machine Learning Foundations GitHub

42:38 Jon Krohn YouTube channel

For our second episode of A4N — the Artificial Neural Network News Network podcast — we discuss how anyone can contribute to the cure for the coronavirus pandemic, mind-controlled prosthetic limbs, and what it takes to succeed as an AI start-up. (Reference Links for video are below)

Our special guest today is Ben Taylor. Ben is the Co-Founder and Chief AI Officer of zeff.ai, an AI product company, and former Chief Data Scientist at HireVue. He is a prolific thinker and innovator, and we’re thrilled to have him as a guest on A4N!

Segment 1 on Tackling Coronaviruses with Machine Learning

1:12 Ben Taylor, Hirevue

2:23 untapt, zeff.ai

4:41 Die Antwoord

5:39 Maryam Khakpour LinkedIn post, Yuval Noah Harari’s book Homo Deus

12:05 CORD-19

16:33 First episode of A4N podcast

17:00 Kaggle Covid-19-related tasks

20:44 Folding@home

Segment 2 on Mind Controlled Prosthetics 36:39

37:45 Reference blog post from University of Michigan

47:41 Gabe Adams: Twitter account and YouTube video

54:27 Norman Doidge book The Brain That Changes Itself

Segment 3 on AI Startups 57:00

57:20 Reference blog post from Andreesen Horowitz

Jon Krohn / A4N YouTube channel

Jon Krohn Twitter

Jon Krohn website for signing up to email newsletter

Jon Krohn LinkedIn

Grant Beyleveld Twitter

Ben Taylor Twitter

Ben Taylor LinkedIn

For our inaugural episode of A4N — the "Artificial Neural Network News Network", a lighthearted podcast covering A.I. advances — we discuss real-time face recognition by London police, cheating in the famed Kaggle data-science competition, the landmark AlphaFold model for predicting protein structure from DNA, and how to become (or at least hire!) an A.I. researcher.

Below’s a detailed breakdown of the episode’s four segments, with time stamps for all of the references that we mentioned.

Segment 1 — Global headline news: Intrusive real-time face recognition

In this segment, we discuss a controversial new approach by the Met police force in London, which is to use a (low-performing) facial-recognition system to flag “known criminals” in real-time in train stations.

Segment host: Vince Petaccio II

Reference news article from The Guardian (1:10)

Citizen App (9:10)

Segment 2 — “Sports”: Kaggle Cheating

In this segment, we introduce what the Kaggle data-science competition platform is and how folks (now formerly!) working at the well-known firm H2O.ai cheated to perform well. How unsportspersonlike!

Segment host: Andrew Vlahutin

Reference news article from Towards Data Science (14:38)

Segment 3 — Health: AlphaFold

In this segment, we introduce how DNA encodes proteins that do all of the work in our bodies. We then describe the new AlphaFold algorithm that crushes all of the existing approaches at predicting protein structure from DNA-sequence data. In the benchmark “CASP” competition, AlphaFold correctly predicted the structure of 58.1% of the proteins while the second-best algorithm correctly predicted 7.0% of them.

Segment host: Grant Beyleveld

Reference blog post from DeepMind (26:31)

CASP (30:05)

AlphaGo documentary film (37:20)

Segment 4 — Classifieds: How to become (or hire!) an A.I. Researcher

In this segment, we list ways that you can find hidden-gem A.I. researchers to hire from within a high-demand field. We also list approaches for breaking into the field of A.I. if you come from a non-traditional background, e.g., you don’t already have a PhD in machine learning or statistics.

Segment host: Jon

How to Hire Smarter than the Market (38:33)

Getting Hired in AI as Self-Taught Researcher (40:39)

Deep Learning book by Goodfellow et al. (41:30)